runBenchmarks {DHARMa} R Documentation

## Benchmark calculations

### Description

This function runs statistical benchmarks, including Power / Type I error simulations for an arbitrary test with a control parameter

### Usage

runBenchmarks(calculateStatistics, controlValues = NULL, nRep = 10,
alpha = 0.05, parallel = FALSE, exportGlobal = F, ...)


### Arguments

 calculateStatistics the statistics to be benchmarked. Should return one value, or a vector of values. If controlValues are given, must accept a parameter control controlValues optionally, a vector with a control parameter (e.g. to vary the strength of a problem the test should be specific to). See help for an example nRep number of replicates per level of the controlValues alpha significance level parallel whether to use parallel computations. Possible values are F, T (sets the cores automatically to number of available cores -1), or an integer number for the number of cores that should be used for the cluster exportGlobal whether the global environment should be exported to the parallel nodes. This will use more memory. Set to true only if you function calculate statistics depends on other functions or global variables. ... additional parameters to calculateStatistics

### Value

A object with list structure of class DHARMaBenchmark. Contains an entry simulations with a matrix of simulations, and an entry summaries with an list of summaries (significant (T/F), mean, p-value for KS-test uniformity). Can be plotted with plot.DHARMaBenchmark

### Note

The benchmark function in DHARMa are intended for development purposes, and for users that want to test / confirm the properties of functions in DHARMa. If you are running an applied data analysis, they are probably of little use.

### Author(s)

Florian Hartig

plot.DHARMaBenchmark

### Examples

# define a function that will run a simulation and return a number of statistics, typically p-values
returnStatistics <- function(control = 0){
testData = createData(sampleSize = 20, family = poisson(), overdispersion = control,
randomEffectVariance = 0)
fittedModel <- glm(observedResponse ~ Environment1, data = testData, family = poisson())
res <- simulateResiduals(fittedModel = fittedModel, n = 250)
out <- c(testUniformity(res, plot = FALSE)$p.value, testDispersion(res, plot = FALSE)$p.value)
return(out)
}

# testing a single return
returnStatistics()

# running benchmark for a fixed simulation, increase nRep for sensible results
out = runBenchmarks(returnStatistics, nRep = 5)

# plotting results depend on whether a vector or a single value is provided for control
plot(out)

## Not run:

# running benchmark with varying control values
out = runBenchmarks(returnStatistics, controlValues = c(0,0.5,1), nRep = 100)
plot(out)

# running benchmark can be done using parallel cores
out = runBenchmarks(returnStatistics, nRep = 100, parallel = TRUE)
out = runBenchmarks(returnStatistics, controlValues = c(0,0.5,1), nRep = 10, parallel = TRUE)

# Alternative plot function using vioplot, provides nicer pictures

plot.DHARMaBenchmark <- function(x, ...){

if(length(x$controlValues)== 1){ vioplot::vioplot(x$simulations[,x$nSummaries:1], las = 2, horizontal = T, side = "right", areaEqual = F, main = "p distribution under H0", ylim = c(-0.15,1), ...) abline(v = 1, lty = 2) abline(v = c(0.05, 0), lty = 2, col = "red") text(-0.1, x$nSummaries:1, labels = x$summaries$propSignificant[-1])

}else{
res = x$summaries$propSignificant
matplot(res$controlValues, res[,-1], type = "l", main = "Power analysis", ylab = "Power", ...) legend("bottomright", colnames(res[,-1]), col = 1:x$nSummaries, lty = 1:x\$nSummaries, lwd = 2)

}
}

## End(Not run)


[Package DHARMa version 0.4.6 Index]